





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:BertModel      ,				snake_case__	:str      ,				snake_case__	:str  )   ->    int:
  _lowercase						    =			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  _lowercase						    =			(
      ('layer.', 'layer_'),
      ('word_embeddings.weight', 'word_embeddings'),
      ('position_embeddings.weight', 'position_embeddings'),
      ('token_type_embeddings.weight', 'token_type_embeddings'),
      ('.', '/'),
      ('LayerNorm/weight', 'LayerNorm/gamma'),
      ('LayerNorm/bias', 'LayerNorm/beta'),
      ('weight', 'kernel'),
  )

  if not os.path.isdir(snake_case__  ):
    os.makedirs(snake_case__  )

  _lowercase						    =			model.state_dict()

  def to_tf_var_name(snake_case__	:str  ):
    for patt, repl in iter(snake_case__  ):
      _lowercase						    =			name.replace(snake_case__      ,				snake_case__  )
    return F"""bert/{name}"""

  def create_tf_var(snake_case__	:np.ndarray      ,				snake_case__	:str      ,				snake_case__	:tf.Session  ):
    _lowercase						    =			tf.dtypes.as_dtype(tensor.dtype  )
    _lowercase						    =			tf.get_variable(dtype=snake_case__      ,				shape=tensor.shape      ,				name=snake_case__      ,				initializer=tf.zeros_initializer()  )
    session.run(tf.variables_initializer([tf_var]  )  )
    session.run(snake_case__  )
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      _lowercase						    =			to_tf_var_name(snake_case__  )
      _lowercase						    =			state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose  ):
        _lowercase						    =			torch_tensor.T
      _lowercase						    =			create_tf_var(tensor=snake_case__      ,				name=snake_case__      ,				session=snake_case__  )
      tf.keras.backend.set_value(snake_case__      ,				snake_case__  )
      _lowercase						    =			session.run(snake_case__  )
      print(F"""Successfully created {tf_name}: {np.allclose(snake_case__      ,				snake_case__  )}"""  )

    _lowercase						    =			tf.train.Saver(tf.trainable_variables()  )
    saver.save(snake_case__      ,				os.path.join(snake_case__      ,				model_name.replace('-'      ,				'_'  ) + '.ckpt'  )  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any=None  )   ->    List[str]:
  _lowercase						    =			argparse.ArgumentParser()
  parser.add_argument('--model_name'      ,				type=snake_case__      ,				required=snake_case__      ,				help='model name e.g. bert-base-uncased'  )
  parser.add_argument(
      '--cache_dir'      ,				type=snake_case__      ,				default=snake_case__      ,				required=snake_case__      ,				help='Directory containing pytorch model'  )
  parser.add_argument('--pytorch_model_path'      ,				type=snake_case__      ,				required=snake_case__      ,				help='/path/to/<pytorch-model-name>.bin'  )
  parser.add_argument('--tf_cache_dir'      ,				type=snake_case__      ,				required=snake_case__      ,				help='Directory in which to save tensorflow model'  )
  _lowercase						    =			parser.parse_args(snake_case__  )

  _lowercase						    =			BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name      ,				state_dict=torch.load(args.pytorch_model_path  )      ,				cache_dir=args.cache_dir      ,				)

  convert_pytorch_checkpoint_to_tf(model=snake_case__      ,				ckpt_dir=args.tf_cache_dir      ,				model_name=args.model_name  )


if __name__ == "__main__":
      main()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase    :   Optional[int]						=			{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['MobileViTFeatureExtractor']
		lowercase    :   Tuple						=			['MobileViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileViTForImageClassification',
		    'MobileViTForSemanticSegmentation',
		    'MobileViTModel',
		    'MobileViTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileViTForImageClassification',
		    'TFMobileViTForSemanticSegmentation',
		    'TFMobileViTModel',
		    'TFMobileViTPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)